1 Introduction
1.1 Usage of this file
This file serves to be a supplementary document that describes all the statistics results performed for this project. It may help to test some new questions that are not included in the corresponding slides.
1.2 Experiment designs
This file displays the results of the FaceWord project (data collected at NYU). There are two experiments in this project. In Experiment 1, Chinese participants viewed Chinese faces and characters in four conditions (Layout: intact, exchange [top and bottom parts were switched], top and bottom) and completed an additional localizer (Chinese faces, Chinese characters, objects, scrambled objects). In Experiment 2, English speakers viewed Chinese characters and English words in four conditions (Layout: intact, exchange, top [top parts of Chinese characters; left two letters for English words] and bottom [bottom parts of Chinese characters; right four letters for English words]) and completed an additional localizer (Caucasian faces, English words, objects, scrambled objects).
1.3 Introduction to the analyses included in this file
For the main runs, analysis is conducted for each ROI separately (FFA1, FFA2, VWFA, LOC).
For each ROI, three analyses are performed:
- Univariate analysis (Repeated-measures ANOVA) is performed to compare the neural responses (beta values) of different conditions.
- E1: 2(Chinese faces vs. Chinese Characters) * 4 (intact, exchange, top vs. bottom);
- E2: 2(Chinese characters vs. English words) * 4 (intact, exchange, top vs. bottom).
- Multivariate pattern analysis (MVPA) with
libsvmis used to decode different condition pairs (see below) and one-tail one-sample t-tests is used to test if the pair of conditions can be decoded [whether the accuracy is significantly larger than the chancel level (0.5); one-tail one-sample t-tests].- Pairs in E1:
- face_intact vs. word_intact;
- face_intact vs. face_exchange;
- face_top vs. face_bottom;
- word_intact vs. word_exchange;
- word_top vs. word_bottom.
- Pairs in E2:
- Chinese_intact vs. English_intact;
- Chinese_intact vs. Chinese_exchange;
- Chinese_top vs. Chinese_bottom;
- English_intact vs. English_exchange;
- English_top vs. English_bottom.
- Pairs in E1:
- Similarity of top+bottom to intact vs. exchange: The dependent variable is the probability of top+bottom was decoded as Exchange conditions. Two-tail one-sample t-tests is used to test if top+bottom is more similar to exchange relative to intact.
- If the pattern of top+bottom is more similar to that of exchange relative to intact, the probability (of being decoded as exchange) should be significantly larger than the chance level (0.5).
- If the pattern of top+bottom is more similar to that of intact relative to exchange, the probability (of being decoded as exchange) should be significantly smaller than the chance level (0.5).
1.4 How the labels are created/defined for each ROI?
- Identify the global maxima for each ROI based on the coordinates in the literature.
- Dilate the region centering at the global maxima until it reaches the pre-defined size (100mm^2), during which the vertices are masked by a pre-defined lable at f13 (i.e., p < .05). In other words, the p-values for all vertices in the labels are smaller than .05 (uncorrected).
1.5 How is the probability of top+bottom being decoded as exchange calculated?
The probability was estimated for each particiapnt separately:
- The patterns of top and bottom are combined with three different weights (0.5/0.5, 0.25/0.75, 0.75/0.25).
- Supported Vector Machine (
libsvm) is trained with the patterns of intact vs. exchange (10 runs). - The trained model is used to predict the probability of the combined patterns being decoded as exchange [for each run separately].
- The probability of top+bottom being decoded as exchange for each participant is calculated by averaging the probability for each run.
2 Preparations
3 Experiment 1: Chinese faces and Chinese characters for Chinese participants
3.1 Load and clean data
3.1.1 Label (ROI) information
3.1.1.1 Size of labels
The above table displays the size (in mm2) of each label for each participant. (NA denotes that this label is not available for that particiapnt.)
3.1.1.2 Number of vertices for each label
The above table displays the number of vertices for each label and each participant. (NA denotes that this label is not available for that particiapnt.)
3.1.1.3 Number of participants for each ROI
3.1.1.4 Number of remaining participants
The above table dispalys the number of participants included in the following analyses for each ROI. (VWFA is only found on the left hemisphere.)
3.1.2 Data for univariate analyses
3.1.3 Data of decoding
3.1.4 Data for the Similarity of top + bottom
3.2 Label:FFA1
3.2.1 Univariate analyses
3.2.1.1 rm-ANOVA
3.2.1.1.1 Left FFA1
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 11 0.19 9.30 * .13 .01
## 2 Layout 2.33, 25.60 0.03 10.14 *** .05 .0003
## 3 FaceWord:Layout 2.41, 26.50 0.02 2.52 + .01 .09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.271 0.0888 11 3.050 0.0111
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.0741 0.0416 33 1.781 0.3002
## intact - top 0.2140 0.0416 33 5.141 0.0001
## intact - bottom 0.1566 0.0416 33 3.761 0.0035
## exchange - top 0.1398 0.0416 33 3.359 0.0102
## exchange - bottom 0.0824 0.0416 33 1.980 0.2159
## top - bottom -0.0574 0.0416 33 -1.379 0.5209
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.34161 0.1009 17.8 3.387 0.0033
## exchange . faces - words 0.21131 0.1009 17.8 2.095 0.0507
## top . faces - words 0.35013 0.1009 17.8 3.471 0.0028
## bottom . faces - words 0.18019 0.1009 17.8 1.786 0.0911
## . faces intact - exchange 0.13929 0.0571 65.7 2.440 0.0174
## . faces intact - top 0.20971 0.0571 65.7 3.674 0.0005
## . faces intact - bottom 0.23728 0.0571 65.7 4.157 0.0001
## . faces exchange - top 0.07042 0.0571 65.7 1.234 0.2217
## . faces exchange - bottom 0.09798 0.0571 65.7 1.716 0.0908
## . faces top - bottom 0.02756 0.0571 65.7 0.483 0.6308
## . words intact - exchange 0.00899 0.0571 65.7 0.157 0.8754
## . words intact - top 0.21823 0.0571 65.7 3.823 0.0003
## . words intact - bottom 0.07586 0.0571 65.7 1.329 0.1885
## . words exchange - top 0.20924 0.0571 65.7 3.665 0.0005
## . words exchange - bottom 0.06687 0.0571 65.7 1.171 0.2457
## . words top - bottom -0.14238 0.0571 65.7 -2.494 0.01523.2.1.1.2 Right FFA1
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 16 0.34 25.86 *** .20 .0001
## 2 Layout 2.56, 40.95 0.06 4.41 * .02 .01
## 3 FaceWord:Layout 2.52, 40.29 0.06 4.63 * .02 .01
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.505 0.0994 16 5.085 0.0001
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.15731 0.0536 48 2.933 0.0255
## intact - top 0.16926 0.0536 48 3.156 0.0142
## intact - bottom 0.14869 0.0536 48 2.773 0.0382
## exchange - top 0.01195 0.0536 48 0.223 0.9960
## exchange - bottom -0.00861 0.0536 48 -0.161 0.9985
## top - bottom -0.02057 0.0536 48 -0.383 0.9806
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.74207 0.1182 30.3 6.279 <.0001
## exchange . faces - words 0.40872 0.1182 30.3 3.458 0.0016
## top . faces - words 0.41798 0.1182 30.3 3.537 0.0013
## bottom . faces - words 0.45219 0.1182 30.3 3.826 0.0006
## . faces intact - exchange 0.32398 0.0749 95.9 4.327 <.0001
## . faces intact - top 0.33130 0.0749 95.9 4.425 <.0001
## . faces intact - bottom 0.29363 0.0749 95.9 3.922 0.0002
## . faces exchange - top 0.00732 0.0749 95.9 0.098 0.9223
## . faces exchange - bottom -0.03035 0.0749 95.9 -0.405 0.6861
## . faces top - bottom -0.03767 0.0749 95.9 -0.503 0.6160
## . words intact - exchange -0.00937 0.0749 95.9 -0.125 0.9007
## . words intact - top 0.00722 0.0749 95.9 0.096 0.9234
## . words intact - bottom 0.00375 0.0749 95.9 0.050 0.9601
## . words exchange - top 0.01658 0.0749 95.9 0.222 0.8252
## . words exchange - bottom 0.01312 0.0749 95.9 0.175 0.8612
## . words top - bottom -0.00346 0.0749 95.9 -0.046 0.96323.2.1.2 Plot
The above figure shows the neural respones (beta values) in FFA1 for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: , p < .05; **, p <.001
3.2.2 Decoding
3.2.2.1 One-sample t-test
3.2.2.2 Plot
The above figure shows the decoding accuracy in FFA1 for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: , p < .05; , p < .01; , p <.001
3.2.3 Similarity of top + bottom to intact vs. exchange
3.2.3.1 One-sample t-test
3.2.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in FFA1. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
3.3 Label:FFA2
3.3.1 Univariate analyses
3.3.1.1 rm-ANOVA
3.3.1.1.1 Left FFA2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 13 0.07 1.50 .006 .24
## 2 Layout 2.52, 32.76 0.02 10.40 *** .03 .0001
## 3 FaceWord:Layout 2.46, 31.96 0.03 0.44 .002 .69
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.0606 0.0494 13 1.226 0.2419
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.1112 0.0338 39 3.291 0.0110
## intact - top 0.1868 0.0338 39 5.526 <.0001
## intact - bottom 0.0842 0.0338 39 2.493 0.0769
## exchange - top 0.0755 0.0338 39 2.235 0.1318
## exchange - bottom -0.0270 0.0338 39 -0.798 0.8548
## top - bottom -0.1025 0.0338 39 -3.033 0.0214
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.11378 0.0720 41.2 1.581 0.1216
## exchange . faces - words 0.05975 0.0720 41.2 0.830 0.4113
## top . faces - words 0.05176 0.0720 41.2 0.719 0.4761
## bottom . faces - words 0.01708 0.0720 41.2 0.237 0.8137
## . faces intact - exchange 0.13824 0.0545 74.1 2.537 0.0133
## . faces intact - top 0.21776 0.0545 74.1 3.997 0.0002
## . faces intact - bottom 0.13260 0.0545 74.1 2.434 0.0174
## . faces exchange - top 0.07952 0.0545 74.1 1.460 0.1486
## . faces exchange - bottom -0.00564 0.0545 74.1 -0.104 0.9178
## . faces top - bottom -0.08516 0.0545 74.1 -1.563 0.1223
## . words intact - exchange 0.08420 0.0545 74.1 1.546 0.1265
## . words intact - top 0.15574 0.0545 74.1 2.858 0.0055
## . words intact - bottom 0.03589 0.0545 74.1 0.659 0.5121
## . words exchange - top 0.07153 0.0545 74.1 1.313 0.1932
## . words exchange - bottom -0.04832 0.0545 74.1 -0.887 0.3780
## . words top - bottom -0.11985 0.0545 74.1 -2.200 0.03093.3.1.1.2 Right FFA2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 14 0.17 19.27 *** .14 .0006
## 2 Layout 2.47, 34.55 0.02 10.20 *** .03 .0001
## 3 FaceWord:Layout 1.96, 27.47 0.04 3.89 * .01 .03
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.327 0.0746 14 4.390 0.0006
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.1897 0.0366 42 5.179 <.0001
## intact - top 0.1549 0.0366 42 4.230 0.0007
## intact - bottom 0.1016 0.0366 42 2.775 0.0395
## exchange - top -0.0347 0.0366 42 -0.948 0.7791
## exchange - bottom -0.0880 0.0366 42 -2.404 0.0919
## top - bottom -0.0533 0.0366 42 -1.456 0.4728
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.49653 0.0899 27.7 5.523 <.0001
## exchange . faces - words 0.24478 0.0899 27.7 2.723 0.0111
## top . faces - words 0.28897 0.0899 27.7 3.214 0.0033
## bottom . faces - words 0.27898 0.0899 27.7 3.103 0.0044
## . faces intact - exchange 0.31555 0.0550 82.9 5.737 <.0001
## . faces intact - top 0.25873 0.0550 82.9 4.704 <.0001
## . faces intact - bottom 0.21040 0.0550 82.9 3.826 0.0003
## . faces exchange - top -0.05682 0.0550 82.9 -1.033 0.3045
## . faces exchange - bottom -0.10515 0.0550 82.9 -1.912 0.0594
## . faces top - bottom -0.04832 0.0550 82.9 -0.879 0.3821
## . words intact - exchange 0.06380 0.0550 82.9 1.160 0.2494
## . words intact - top 0.05116 0.0550 82.9 0.930 0.3550
## . words intact - bottom -0.00715 0.0550 82.9 -0.130 0.8969
## . words exchange - top -0.01264 0.0550 82.9 -0.230 0.8189
## . words exchange - bottom -0.07094 0.0550 82.9 -1.290 0.2007
## . words top - bottom -0.05831 0.0550 82.9 -1.060 0.29213.3.1.2 Plot
The above figure shows the neural respones (beta values) in FFA2 for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: , p < .05; **, p <.001
3.3.2 Decoding
3.3.2.1 One-sample t-test
3.3.2.2 Plot
The above figure shows the decoding accuracy in FFA2 for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: , p < .05; **, p <.001
3.3.3 Similarity of top + bottom to intact vs. exchange
3.3.3.1 One-sample t-test
3.3.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in FFA2. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
3.4 Label: left Visual Word Form Area (VWFA)
3.4.1 Univariate analyses
3.4.1.1 rm-ANOVA
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 16 0.16 59.65 *** .13 <.0001
## 2 Layout 2.50, 39.98 0.03 2.93 + .004 .05
## 3 FaceWord:Layout 2.49, 39.82 0.02 8.46 *** .008 .0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words -0.538 0.0696 16 -7.724 <.0001
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange -0.01212 0.0403 48 -0.301 0.9904
## intact - top 0.09355 0.0403 48 2.321 0.1075
## intact - bottom 0.00265 0.0403 48 0.066 0.9999
## exchange - top 0.10567 0.0403 48 2.622 0.0549
## exchange - bottom 0.01477 0.0403 48 0.366 0.9830
## top - bottom -0.09090 0.0403 48 -2.255 0.1232
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.44403 0.0812 28.4 -5.468 <.0001
## exchange . faces - words -0.67875 0.0812 28.4 -8.358 <.0001
## top . faces - words -0.39309 0.0812 28.4 -4.841 <.0001
## bottom . faces - words -0.63536 0.0812 28.4 -7.824 <.0001
## . faces intact - exchange 0.10524 0.0528 93.5 1.993 0.0492
## . faces intact - top 0.06808 0.0528 93.5 1.289 0.2005
## . faces intact - bottom 0.09831 0.0528 93.5 1.862 0.0658
## . faces exchange - top -0.03716 0.0528 93.5 -0.704 0.4834
## . faces exchange - bottom -0.00692 0.0528 93.5 -0.131 0.8960
## . faces top - bottom 0.03023 0.0528 93.5 0.573 0.5684
## . words intact - exchange -0.12949 0.0528 93.5 -2.452 0.0161
## . words intact - top 0.11902 0.0528 93.5 2.254 0.0266
## . words intact - bottom -0.09302 0.0528 93.5 -1.761 0.0814
## . words exchange - top 0.24850 0.0528 93.5 4.706 <.0001
## . words exchange - bottom 0.03647 0.0528 93.5 0.691 0.4916
## . words top - bottom -0.21204 0.0528 93.5 -4.015 0.00013.4.1.2 Plot
The above figure shows the neural respones (beta values) in VWFA for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: *, p < .05
3.4.2 Decoding
3.4.2.1 One-sample t-test
3.4.2.2 Plot
The above figure shows the decoding accuracy in VWFA for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: ***, p <.001
3.4.3 Similarity of top + bottom to intact vs. exchange
3.4.3.1 One-sample t-test
3.4.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in VWFA. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
3.5 Label:Lateral Occipital Cortex
3.5.1 Univariate analyses
3.5.1.1 rm-ANOVA
3.5.1.1.1 Left LO
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 17 0.45 12.63 ** .03 .002
## 2 Layout 1.78, 30.24 0.09 0.51 .0004 .58
## 3 FaceWord:Layout 2.63, 44.64 0.04 1.05 .0006 .37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words -0.399 0.112 17 -3.554 0.0024
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.00832 0.0529 51 0.157 0.9986
## intact - top 0.06032 0.0529 51 1.139 0.6672
## intact - bottom 0.02557 0.0529 51 0.483 0.9626
## exchange - top 0.05200 0.0529 51 0.982 0.7602
## exchange - bottom 0.01725 0.0529 51 0.326 0.9879
## top - bottom -0.03475 0.0529 51 -0.656 0.9128
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.35647 0.1238 24.7 -2.878 0.0081
## exchange . faces - words -0.45834 0.1238 24.7 -3.701 0.0011
## top . faces - words -0.33688 0.1238 24.7 -2.720 0.0118
## bottom . faces - words -0.44556 0.1238 24.7 -3.598 0.0014
## . faces intact - exchange 0.05925 0.0679 97.5 0.873 0.3851
## . faces intact - top 0.05053 0.0679 97.5 0.744 0.4586
## . faces intact - bottom 0.07011 0.0679 97.5 1.032 0.3044
## . faces exchange - top -0.00872 0.0679 97.5 -0.128 0.8980
## . faces exchange - bottom 0.01086 0.0679 97.5 0.160 0.8733
## . faces top - bottom 0.01958 0.0679 97.5 0.288 0.7736
## . words intact - exchange -0.04262 0.0679 97.5 -0.628 0.5317
## . words intact - top 0.07012 0.0679 97.5 1.033 0.3044
## . words intact - bottom -0.01897 0.0679 97.5 -0.279 0.7805
## . words exchange - top 0.11273 0.0679 97.5 1.660 0.1001
## . words exchange - bottom 0.02364 0.0679 97.5 0.348 0.7285
## . words top - bottom -0.08909 0.0679 97.5 -1.312 0.19263.5.1.1.2 Right LO
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 18 0.15 7.85 * .010 .01
## 2 Layout 2.37, 42.59 0.05 4.51 * .004 .01
## 3 FaceWord:Layout 2.54, 45.67 0.04 0.99 .0008 .40
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words -0.176 0.0628 18 -2.802 0.0118
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.0912 0.0454 54 2.010 0.1971
## intact - top 0.1576 0.0454 54 3.474 0.0055
## intact - bottom 0.0395 0.0454 54 0.871 0.8198
## exchange - top 0.0664 0.0454 54 1.464 0.4660
## exchange - bottom -0.0517 0.0454 54 -1.139 0.6672
## top - bottom -0.1181 0.0454 54 -2.603 0.0560
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.0867 0.0814 44.1 -1.064 0.2929
## exchange . faces - words -0.2075 0.0814 44.1 -2.548 0.0144
## top . faces - words -0.2077 0.0814 44.1 -2.550 0.0143
## bottom . faces - words -0.2018 0.0814 44.1 -2.479 0.0171
## . faces intact - exchange 0.1516 0.0620 107.5 2.443 0.0162
## . faces intact - top 0.2181 0.0620 107.5 3.514 0.0006
## . faces intact - bottom 0.0971 0.0620 107.5 1.565 0.1206
## . faces exchange - top 0.0665 0.0620 107.5 1.071 0.2864
## . faces exchange - bottom -0.0545 0.0620 107.5 -0.878 0.3817
## . faces top - bottom -0.1210 0.0620 107.5 -1.950 0.0538
## . words intact - exchange 0.0307 0.0620 107.5 0.496 0.6213
## . words intact - top 0.0971 0.0620 107.5 1.564 0.1207
## . words intact - bottom -0.0181 0.0620 107.5 -0.291 0.7712
## . words exchange - top 0.0663 0.0620 107.5 1.069 0.2875
## . words exchange - bottom -0.0488 0.0620 107.5 -0.787 0.4330
## . words top - bottom -0.1152 0.0620 107.5 -1.856 0.06623.5.1.2 Plot
The above figure shows the neural respones (beta values) in LO for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: *, p < .05
3.5.2 Decoding
3.5.2.1 One-sample t-test
3.5.2.2 Plot
The above figure shows the decoding accuracy in LO for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: , p < .01; *, p <.001
3.5.3 Similarity of top + bottom to intact vs. exchange
3.5.3.1 One-sample t-test
3.5.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in LO. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
4 Versions of packages used
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tools stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.2.5 magrittr_1.5 emmeans_1.4.2 lmerTest_3.1-0 afex_0.25-1 lme4_1.1-21 Matrix_1.2-18 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.5 purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_2.1.3 ggplot2_3.3.0 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.1 jsonlite_1.6.1 splines_3.6.3 carData_3.0-3 modelr_0.1.5 assertthat_0.2.1 cellranger_1.1.0 yaml_2.2.1 numDeriv_2016.8-1.1 pillar_1.4.3 backports_1.1.5 lattice_0.20-38 glue_1.3.2 digest_0.6.25 ggsignif_0.6.0 rvest_0.3.5 minqa_1.2.4 colorspace_1.4-1 cowplot_1.0.0 htmltools_0.4.0 plyr_1.8.6 pkgconfig_2.0.3
## [23] broom_0.5.3.9000 haven_2.2.0 xtable_1.8-4 mvtnorm_1.0-11 scales_1.0.0 openxlsx_4.1.3 rio_0.5.16 generics_0.0.2 car_3.0-5 ellipsis_0.3.0 withr_2.1.2 cli_2.0.2 crayon_1.3.4 readxl_1.3.1 estimability_1.3 evaluate_0.14 fansi_0.4.1 nlme_3.1-144 MASS_7.3-51.5 xml2_1.2.2 foreign_0.8-75 data.table_1.12.6
## [45] hms_0.5.2 lifecycle_0.2.0 munsell_0.5.0 zip_2.0.4 compiler_3.6.3 rlang_0.4.5 grid_3.6.3 nloptr_1.2.1 rstudioapi_0.10 labeling_0.3 rmarkdown_2.1 boot_1.3-24 gtable_0.3.0 abind_1.4-5 curl_4.2 reshape2_1.4.3 R6_2.4.1 lubridate_1.7.4 knitr_1.28 stringi_1.4.6 parallel_3.6.3 Rcpp_1.0.4
## [67] vctrs_0.2.4 tidyselect_1.0.0 xfun_0.12 coda_0.19-3A work by Haiyang Jin